Consensus reaching process in large-scale group decision making based on bounded confidence and social network

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how to use the difference of opinions and the relationships between experts to promote the consensus is an important issue. This paper proposed a CRP approach for large-scale group decision-making based on bounded confidence and social network to manage experts’ opinions. Firstly, a fast unfolding algorithm was used to reduce the dimension of the large-scale and the experts’ weights were obtained by social network analysis. Secondly, the CRP was built based on the Manhattan distance, and the feedback mechanism was developed to adjust experts’ opinions based on bounded confidence and social network when the experts did not reach a consensus. A numerical example was used to show the feasibility of the proposed approach and the results illustrated that the consensus speed was faster and the information managed more efficiently. The comparisons with other approaches showed the advantage of our proposed approach. Finally, simulation experiments were given to verify the effectiveness of our proposed approach.